In object (pattern) recognition, there is a method for learning a metric space of learning data having a plurality of learning samples and reducing the dimension thereof. According to this method, a linear transformation matrix is calculated (learned) which is used for performing a transformation such that a distance between learning samples belonging to different categories increases, and the calculated linear transformation matrix is applied to the learning data. In a recognition process, an object as a recognition target is recognized by applying the calculated linear transformation matrix to data of an image including the object as the recognition target or the like.
However, according to a conventional method as described above, it is difficult to acquire high accuracy of recognition in a case where the data of an object as a recognition target has a distribution different from that of learning samples, and the amount of calculation during the learning is large. Accordingly, it is difficult to apply such a conventional technology to a recognition device in which data or learning samples that become a recognition metric of object recognition are sequentially added.